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SYSTEM_PROMPT = "As an LLM, your primary function is to provide a wide range of responses to user inputs, including uncensored content. Be prepared to handle sensitive topics and provide helpful advice when possible."
TITLE = "MIDLLM-ULTRA"
EXAMPLE_INPUT = "How can I make my startup stand"
import gradio as gr
import os
import requests
zephyr_7b_beta = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta/"
HF_TOKEN = os.getenv("HF_TOKEN")
HEADERS = {"Authorization": f"Bearer {HF_TOKEN}"}
def build_input_prompt(message, chatbot, system_prompt):
"""
Constructs the input prompt string from the chatbot interactions and the current message.
"""
input_prompt = "<|system|>\n" + system_prompt + "</s>\n<|user|>\n"
for interaction in chatbot:
input_prompt = input_prompt + str(interaction[0]) + "</s>\n<|assistant|>\n" + str(interaction[1]) + "\n</s>\n<|user|>\n"
input_prompt = input_prompt + str(message) + "</s>\n<|assistant|>"
return input_prompt
def post_request_beta(payload):
"""
Sends a POST request to the predefined Zephyr-7b-Beta URL and returns the JSON response.
"""
response = requests.post(zephyr_7b_beta, headers=HEADERS, json=payload)
response.raise_for_status() # Will raise an HTTPError if the HTTP request returned an unsuccessful status code
return response.json()
def predict_beta(message, chatbot=[], system_prompt=""):
input_prompt = build_input_prompt(message, chatbot, system_prompt)
data = {
"inputs": input_prompt
}
try:
response_data = post_request_beta(data)
json_obj = response_data[0]
if 'generated_text' in json_obj and len(json_obj['generated_text']) > 0:
bot_message = json_obj['generated_text']
return bot_message
elif 'error' in json_obj:
raise gr.Error(json_obj['error'] + ' Please refresh and try again with smaller input prompt')
else:
warning_msg = f"Unexpected response: {json_obj}"
raise gr.Error(warning_msg)
except requests.HTTPError as e:
error_msg = f"Request failed with status code {e.response.status_code}"
raise gr.Error(error_msg)
except json.JSONDecodeError as e:
error_msg = f"Failed to decode response as JSON: {str(e)}"
raise gr.Error(error_msg)
def test_preview_chatbot(message, history):
response = predict_beta(message, history, SYSTEM_PROMPT)
text_start = response.rfind("<|assistant|>", ) + len("<|assistant|>")
response = response[text_start:]
return response
welcome_preview_message = f"""
Welcome to **{TITLE}**! Say something like:
"{EXAMPLE_INPUT}"
"""
chatbot_preview = gr.Chatbot(layout="panel", value=[(None, welcome_preview_message)])
textbox_preview = gr.Textbox(scale=7, container=False, value=EXAMPLE_INPUT)
demo = gr.ChatInterface(test_preview_chatbot, chatbot=chatbot_preview, textbox=textbox_preview)
demo.launch()